Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice

Real-world data capture of daily limb loading using force-sensing insoles: Feasibility and lessons learned

Force-sensing insoles are wearable technology that offer an innovative way to measure loading outside of laboratory settings. Few studies, however, have utilized insoles to measure daily loading in real-world settings. This is an ancillary study of a randomized controlled trial examining the effect of weight loss alone, weight loss plus weighted vest, or weight loss plus resistance training on bone health in older adults. The purpose of this ancillary study was to determine the feasibility of using force-sensing insoles to collect daily limb loading metrics, including peak force, impulse, and loading rate. Forty-four participants completed a baseline visit of three, 2-minute walking trials while wearing force-sensing insoles. During month two of the intervention, 37 participants wore insoles for 4 days for 8 waking hours each day. At 6-month follow-up, participants completed three, two-minute walking trials and a satisfaction questionnaire. Criteria for success in feasibility was defined as: a) > 60 % recruitment rate; b) > 80 % adherence rate; c) > 75 % of usable data, and d) > 75 % participant satisfaction. A 77.3 % recruitment rate was achieved, with 44 participants enrolled. Participants wore their insoles an average of 7.4 hours per day, and insoles recorded an average of 5.5 hours per day. Peak force, impulse, and loading rate collected at baseline and follow-up were 100 % usable. During the real-world settings, 87.8 % of data was deemed usable with an average of 1200 min/participant. Lastly, average satisfaction was 80.5 %. These results suggest that force-sensing insoles appears to be feasible to capture real-world limb loading in older adults.

The whole day matters after stroke: Study protocol for a randomized controlled trial investigating the effect of a 'sit less, move more, sleep better' program early after stroke

BACKGROUND

Movement-related behaviours, including prolonged sedentary behaviour, physical inactivity, and poor sleep, are associated with worse functional outcomes poststroke. Addressing these co-dependent behaviours early after stroke may help to optimize recovery and improve overall quality of life for individuals with stroke.

OBJECTIVE

This study aims to determine the feasibility and effect of a 'sit less, move more, sleep better' program early after stroke on functional mobility and global disability outcomes, while also exploring imaging and behavioural markers that may influence walking recovery.

METHODS

The study is an assessor-blinded, single-center, parallel-group, randomized controlled trial to be completed within 24 months from July 12, 2023 to June 30, 2025. We will enroll 50 patients with acute ischemic stroke within 7 days from symptom onset, aged 18 years or older, and with ongoing walking goals. Demographic and stroke characteristics, including stroke risk factors, neuroimaging, and acute stroke treatments, will be determined and documented. All participants will wear an accelerometer for one week at three different time-points (baseline, 6, and 12 weeks) to assess movement-related behaviours. Following randomization, participants in the intervention arm will receive a 'sit less, move more, sleep better' program for up to 1 hour/day, 5 days/week, for 6 weeks to enhance self-efficacy for change. Participants in the control arm will receive usual inpatient and early supported stroke discharge care. The feasibility outcomes will include reach (enrolled/eligible), retention (completed/enrolled), adverse events, and program adherence. Other outcomes at 6 and 12 weeks include the modified Rankin Scale, Timed-Up and Go, movement-related behaviours, walking endurance, gait speed, cognition, stroke severity and quality of life. Mixed-effects models will assess changes in outcomes over time. Compositional associations between movement-related behaviours and outcomes will consider covariates such as imaging markers.

DISCUSSION

Adopting a whole-day approach to poststroke rehabilitation will provide valuable insights into the relationship between optimizing movement-related behaviours early after stroke and their impact on functional outcomes. Through exploring person-specific behavioural and imaging markers, this study may inform precision rehabilitation strategies, and guide clinical decision making for more tailored interventions.

TRIAL REGISTRATION

Clinical Trial registration (ClinicalTrials.gov Identifier: NCT05753761, March 3, 2023).

Applications of wearable sensors in upper extremity MSK conditions: a scoping review

PURPOSE

This scoping review uniquely aims to map the current state of the literature on the applications of wearable sensors in people with or at risk of developing upper extremity musculoskeletal (UE-MSK) conditions, considering that MSK conditions or disorders have the highest rate of prevalence among other types of conditions or disorders that contribute to the need for rehabilitation services.

MATERIALS AND METHODS

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) extension for scoping reviews guideline was followed in this scoping review. Two independent authors conducted a systematic search of four databases, including PubMed, Embase, Scopus, and IEEEXplore. We included studies that have applied wearable sensors on people with or at risk of developing UE-MSK condition published after 2010. We extracted study designs, aims, number of participants, sensor placement locations, sensor types, and number, and outcome(s) of interest from the included studies. The overall findings of our scoping review are presented in tables and diagrams to map an overview of the existing applications.

RESULTS

The final review encompassed 80 studies categorized into clinical population (31 studies), workers' population (31 studies), and general wearable design/performance studies (18 studies). Most were observational, with 2 RCTs in workers' studies. Clinical studies focused on UE-MSK conditions like rotator cuff tear and arthritis. Workers' studies involved industrial workers, surgeons, farmers, and at-risk healthy individuals. Wearable sensors were utilized for objective motion assessment, home-based rehabilitation monitoring, daily activity recording, physical risk characterization, and ergonomic assessments. IMU sensors were prevalent in designs (84%), with a minority including sEMG sensors (16%). Assessment applications dominated (80%), while treatment-focused studies constituted 20%. Home-based applicability was noted in 21% of the studies.

CONCLUSION

Wearable sensor technologies have been increasingly applied to the health care field. These applications include clinical assessments, home-based treatments of MSK disorders, and monitoring of workers' population in non-standardized areas such as work environments. Assessment-focused studies predominate over treatment studies. Additionally, wearable sensor designs predominantly use IMU sensors, with a subset of studies incorporating sEMG and other sensor types in wearable platforms to capture muscle activity and inertial data for the assessment or rehabilitation of MSK conditions.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

Understanding stroke survivors' preferences regarding wearable sensor feedback on functional movement: a mixed-methods study

BACKGROUND

In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine the ease of understanding and movement encouragement of feedback based on wearable sensor data (both arm/hand use and mobility) for stroke survivors and to identify preferences for feedback metrics (mode, content, frequency, and timing).

METHODS

A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of feedback about arm/hand use, step counts and gait symmetry. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change.

RESULTS

Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: (1) Motivation for behavior change, (2) Real-time feedback based on individual goals, and (3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting.

CONCLUSION

The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment.

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8-18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior-inferior shank acceleration (SI shank Acc) and medial-lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions.

Feedback based on experience sampling data: Examples of current approaches and considerations for future research

Methodologies such as the Experience Sampling Method (ESM) or Ecological Momentary Assessment allow the gathering of fine-graded, dynamic, personal data within a patient's daily life. Currently, it is studied whether feedback based on experience sampling data (ESM-based feedback) can be used as a clinical tool to inform shared decision-making in clinical practice. Although the potential of feedback is recognized, little is known on how to generate, use, and implement it. This article (i) presents n = 15 ongoing ESM projects within the Belgian-Dutch network for ESM research wherein ESM-based feedback is provided to various patient populations, and (ii) summarizes qualitative data on experiences with ESM-based feedback of researchers (n = 8) with extensive expertise with ESM (average of 10 years) involved in these ongoing studies. The following aspects appear to be of relevance when providing ESM-based feedback: training for healthcare professionals and researchers, the use of online interfaces and graphical visualizations to present data, and interacting with patients in a face-to-face setting when discussing the contextual relevance and potential implications. Prospectively, research may build on these aspects and create coherent consensus-based guidelines for the use of ESM-based feedback.

Spatiotemporal parameters from remote smartphone-based gait analysis are associated with lower extremity functional scale categories

Objective

Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored.

Methods

Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).

Results

The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization (n = 132) and many variables differ between groups. Velocity was found to have the strongest effect size.

Discussion

When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

BACKGROUND

Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application.

OBJECTIVE

The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates.

METHODS

A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking.

RESULTS

Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t₁₆=4.24, P<.001) and had more variable wrist movement (t₁₆=2.13, P=.049) compared to those in the lowest quartile.

CONCLUSIONS

The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.

Improvement in the Capacity for Activity Versus Improvement in Performance of Activity in Daily Life During Outpatient Rehabilitation

We addressed questions about the potential discrepancy between improvements in activity capacity and improvements in activity performance in daily life. We asked whether this discrepancy is: Common in routine, outpatient care, or an artifact of intervention studies? Unique to upper limb (UL) rehabilitation, or is it seen in walking rehabilitation too? Only seen in persons with stroke, or a broader neurorehabilitation problem?

Physical activity, physical capacity and sedentary behavior among asthma patients

BACKGROUND: Although exercise and daily physical activity (PA) have long been known to benefit patients with chronic disorders, knowledge is limited regarding asthma.

OBJECTIVE: In a Danish setting, our aim was to measure physical activity, sedentary behavior, and physical capacity among patients with asthma. We hypothesized that people with severe asthma would be less active and more sedentary than their mild-moderate counterparts.

METHODS: Adults with asthma were recruited through respiratory outpatient clinics and subsequently examined twice, 4 weeks apart. At each visit, participants underwent a series of lung function tests, questionnaires, and maximum oxygen uptake testing (VO₂max). Between the visits, participants wore an accelerometer continuously for 4 weeks, measuring sedentary time and daily steps. Sixty patients, 27 with mild-moderate asthma (GINA 1–3) and 33 with severe asthma (GINA 4–5), completed both visits and had valid accelerometer measurements.

RESULTS: No significant differences between the two groups were found in sedentary time, number of steps or VO₂max.   VO₂max was significantly correlated with FeNO (r = −0.30, p < 0.05), Short Form-12 Mental Health (r = 0.37, p < 0.05), Asthma Control Questionnaire (r = −0.35, p < 0.05), and Mini Asthma Quality of Life Questionnaire (r = 0.36, p < 0.05).

CONCLUSION: No differences were observed between patients with mild-moderate and severe asthma regarding sedentary behavior, daily steps or level of cardiopulmonary fitness. Furthermore, patients with the highest VO₂max had the higher quality of life scores.

Abbreviations: VO₂max: Maximal Oxygen Uptake; CPET: Cardiopulmonary Exercise Testing; BMI: Body Mass Index; FEV1: Forced Expired Volume in the First Second; FVC: Forced Vital Capacity; PEF: Peak Expiratory Flow; EIB: Exercise-Induced Bronchoconstriction; COPD: Chronic Obstructive Pulmonary Disease; ACQ: Asthma Control Questionnaire; Mini-AQLQ: Mini Asthma Quality of Life Questionnaire; SF-12: Short Form 12 Health Survey; SNOT-22: Sino-Nasal Outcome Test 22; GINA: The Global Initiative for Asthma; CRP: C-reactive Protein; Hgb:Hemoglobin count; EOS: Eosinophil count; EVH: Eucapnic Voluntary Hyperventilation; FeNO: Fractional Exhaled Nitric Oxide; PA: Physical Activity ERS: European Respiratory Society; ATS: American Thoracic Society; CRS: Chronic Rhinosinusitis; AHR: Airway Hyperresponsiveness

Opportunities and challenges in the development of exoskeletons for locomotor assistance

Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.

Is it Feasible to Use a Low-Cost Wearable Sensor for Heart Rate Monitoring within an Upper Limb Training in Spinal Cord Injured Patients?: A Pilot Study

(1) Background: Cervical spinal cord injury (SCI) patients have impairment in the autonomic nervous system, reflected in the cardiovascular adaption level during the performance of upper limb (UL) activities carried out in the rehabilitation process. This adaption level could be measured from the heart rate (HR) by means of wearable technologies. Therefore, the objective was to analyze the feasibility of using Xiaomi Mi Band 5 wristband (XMB5) for HR monitoring in these patients during the performance of UL activities; (2) Methods: The HR measurements obtained from XMB5 were compared to those obtained by the professional medical equipment Nonin LifeSense II capnograph and pulse oximeter (NLII) in static and dynamic conditions. Then, four healthy people and four cervical SCI patients performed a UL training based on six experimental sessions; (3) Results: the correlation between the HR measurements from XMB5 and NLII devices was strong and positive in healthy people (r = 0.921 and r = 0.941 (p < 0.01) in the static and dynamic conditions, respectively). Then, XMB5 was used within the experimental sessions, and the HR oscillation range measured was significantly higher in healthy individuals than in patients; (4) Conclusions: The XMB5 seems to be feasible for measuring the HR in this biomedical application in SCI patients.

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives

BACKGROUND

The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice.

OBJECTIVE

We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.

METHODS

We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach.

RESULTS

Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice.

CONCLUSIONS

These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients

Current outcome measures, including strength/range of motion testing, patient-reported outcomes (PROs), and motor skill testing, may provide inadequate granularity in reflecting functional upper extremity (UE) use after distal radius fracture (DRF) repair. Accelerometry analysis also has shortcomings, namely, an inability to differentiate functional versus nonfunctional movements. The objective of this study was to evaluate the accuracy of machine learning (ML) analyses in capturing UE functional movements based on accelerometry data for patients after DRF repair. In this prospective study, six patients were enrolled 2-6 weeks after DRF open reduction and internal fixation (ORIF). They all performed standardized activities while wearing a wrist accelerometer, and the data were analyzed by an ML algorithm. These activities were also videotaped and evaluated by visual inspection. Our novel ML algorithm was able to predict from accelerometry data whether the limb was performing a movement rated as functional, with accuracy of 90.4% ± 3.6% for within-subject modeling and 79.8% ± 8.9% accuracy for between-subject modeling. The application of ML algorithms to accelerometry data allowed for capture of functional UE activity in patients after DRF open reduction and internal fixation and accurately predicts functional UE use. Such analyses could improve our understanding of recovery and enhance routine postoperative rehabilitation in DRF patients.

Precision rehabilitation: optimizing function, adding value to health care

Precision medicine efforts are underway in many medical disciplines; however, the power of precision rehabilitation has not yet been explored. Precision medicine aims to deliver the right intervention, at the right time, in the right setting, for the right person, ultimately, bolstering the value of the care that we provide. To date precision medicine efforts have rarely focused on function at the level of a person, but precision rehabilitation is poised to change this and bring the focus on function to the broader precision medicine enterprise. To do this, subgroups of individuals must be identified based on their level of function via precise measurement of their abilities in the physical, cognitive, and psychosocial domains. Adoption of electronic health records, advances in data storage and analytics, and improved measurement technology make this shift possible. Here we detail critical components of the precision rehabilitation framework, including 1) the synergistic use of various study designs, 2) the need for standardized functional measurements, 3) the importance of precise and longitudinal measures of function, 4) the utility of comprehensive databases, 5) the importance of predictive analyses, and 6) the need for system and team science. Precision rehabilitation has the potential to revolutionize clinical care, optimize function for all individuals, and magnify the value of rehabilitation in healthcare; however, to reap the benefits of precision rehabilitation, the rehabilitation community must actively pursue this shift.

Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

A Kinematic Data Based Lower Limb Motor Function Evaluation Method for Post-Stroke Rehabilitation

Recent studies have demonstrated that home-based rehabilitation for stroke patients has excellent potential in reducing the cost and enhancing rehabilitation efficiency. Nonetheless, a timely and accurate rehabilitation assessment is required to attain efficacy and provide feedback to both clinicians and patients. In this paper, a lower limb motor function assessment approach based on limb kinematic data has been presented. The kinematic characteristics of lower limbs were quantified into specific evaluation parameters, which were calculated during a set of selected rehabilitation exercises. A body area network composed of two triaxial accelerometers was used to acquire the limb kinematic data of twenty stroke patients and six healthy subjects. While a referenced template was developed using the data from healthy subjects, an empirical score was obtained to evaluate the lower-limb motor function of stroke patients from the calculated parameters. The results have demonstrated that the scoring has a statistically significant strong correlation with the Brunnstrom stage classification, which provides a practical quantitative evaluation approach for home-based rehabilitation for lower limbs of stroke patients.Clinical Relevance- The proposed quality assessment method provides practical technical support for performing early support discharge rehabilitation.

Inertial Measurement of Head Tilt in Rodents: Principles and Applications to Vestibular Research

Inertial sensors are increasingly used in rodent research, in particular for estimating head orientation relative to gravity, or head tilt. Despite this growing interest, the accuracy of tilt estimates computed from rodent head inertial data has never been assessed. Using readily available inertial measurement units mounted onto the head of freely moving rats, we benchmarked a set of tilt estimation methods against concurrent 3D optical motion capture. We show that, while low-pass filtered head acceleration signals only provided reliable tilt estimates in static conditions, sensor calibration combined with an appropriate choice of orientation filter and parameters could yield average tilt estimation errors below 1.5∘ during movement. We then illustrate an application of inertial head tilt measurements in a preclinical rat model of unilateral vestibular lesion and propose a set of metrics describing the severity of associated postural and motor symptoms and the time course of recovery. We conclude that headborne inertial sensors are an attractive tool for quantitative rodent behavioral analysis in general and for the study of vestibulo-postural functions in particular.

Clinicians' perceptions of a potential wearable device for capturing upper limb activity post-stroke: a qualitative focus group study

Background

There is growing interest in the use of wearable devices that track upper limb activity after stroke to help determine and motivate the optimal dose of upper limb practice. The purpose of this study was to explore clinicians’ perceptions of a prospective wearable device that captures upper limb activity to assist in the design of devices for use in rehabilitation practice.

Methods

Four focus groups with 18 clinicians (occupational and physical therapists with stroke practice experience from a hospital or private practice setting) were conducted. Data were analyzed thematically.

Results

Our analysis revealed three themes: (1) “Quantity and quality is ideal” emphasized how an ideal device would capture both quantity and quality of movement; (2) “Most useful outside therapy sessions” described how therapists foresaw using the device outside of therapy sessions to monitor homework adherence, provide self-monitoring of use, motivate greater use and provide biofeedback on movement quality; (3) “User-friendly please” advocated for the creation of a device that was easy to use and customizable, which reflected the client-centered nature of their treatment.

Conclusions

Findings from this study suggest that clinicians support the development of wearable devices that capture upper limb activity outside of therapy for individuals with some reach to grasp ability. Devices that are easy to use and capture both quality and quantity may result in greater uptake in the clinical setting. Future studies examining acceptability of wearable devices for tracking upper limb activity from the perspective of individuals with stroke are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00927-y.

Wearable Activity Monitoring in Day-to-Day Stroke Care: A Promising Tool but Not Widely Used

Physical activity monitoring with wearable technology has the potential to support stroke rehabilitation. Little is known about how physical therapists use and value the use of wearable activity monitors. This cross-sectional study explores the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Over 300 physical therapists in primary and geriatric care and rehabilitation centers in the Netherlands were invited to fill in an online survey that was developed based on previous studies and interviews with experts. In total, 103 complete surveys were analyzed. Out of the 103 surveys, 27% of the respondents were already using activity monitoring. Of the suggested treatment purposes of activity monitoring, 86% were perceived as useful by more than 55% of the therapists. The most recognized barriers to clinical implementation were lack of skills and knowledge of patients (65%) and not knowing what brand and type of monitor to choose (54%). Of the non-users, 79% were willing to use it in the future. In conclusion, although the concept of remote activity monitoring was perceived as useful, it was not widely adopted by physical therapists involved in stroke care. To date, skills, beliefs, and attitudes of individual therapists determine the current use of wearable technology.

Leveraging insights from behavioral economics to improve mobility for adults with stroke: Design and rationale of the BE Mobile clinical trial

Physical inactivity post-stroke can negatively impact long-term health outcomes and contribute to cardiovascular deconditioning, muscle loss, and increased risk for recurrent stroke. The limited number of interventions designed to improve daily physical activity post-stroke have lacked precision in step goals, are resource intensive, and difficult to scale. The purpose of the Leveraging Insights from Behavioral Economics to Improve Mobility for Adults with Stroke (BE Mobile) trial is to examine the preliminary effectiveness of a novel gamification with social incentives intervention for improving physical activity post-stroke. This trial includes adults who have experienced an ischemic or hemorrhagic stroke ≥3 months prior to the time of recruitment who are randomized to a control or gamification arm. All participants receive a Fitbit Inspire 2 wearable device to quantify daily steps and complete a 2-week baseline run-in period followed by an 8-week intervention period. All participants select a daily step goal and the gamification arm is enrolled in a game with loss-framed points and levels to help participants achieve their daily step goal. Participants in the gamification arm also select a support partner who receives weekly updates on their progress in the game. The primary outcome is change in daily steps from baseline during the intervention period. The secondary outcome is difference in the proportion of days participants achieved their daily step goal. Results from this trial will inform future, larger studies that leverage insights from behavioral economics to help improve daily physical activity post-stroke. Trial registration: NCT #04607811.

Monitoring daily shoulder activity before and after reverse total shoulder arthroplasty using inertial measurement units

BACKGROUND

The purpose of this study was to use at-home, portable, continuous monitoring technologies to record arm motion and activity preoperatively and postoperatively after reverse total shoulder arthroplasty (RTSA).

METHODS

Thirty-three patients indicated for RTSA were monitored preoperatively and 3 and 12 months postoperatively. Inertial measurement units were placed on the sternum and upper arm of the operative limb, recording humeral motion relative to the torso for the duration of a waking day. Elevation events per hour (EE/h) > 90°, time spent at >90°, and activity intensity were calculated and compared between time points. Patient-reported outcome measures were also collected at all time points.

RESULTS

At 3 (P = .040) and 12 (P = .010) months after RTSA, patients demonstrated a significantly greater number of EE/h > 90° compared with preoperatively. There were no significant differences (P ≥ .242) in the amount of time spent at different elevation angles at any time point or in arm activity intensity. Overall, 95% of the day was spent at elevation angles < 60°, and 90% of the day was spent in a low- or moderate-intensity state. Pearson correlations demonstrated relationships between forward elevation and the number of EE/h (r = 0.395, P = .001) and the number of EE/h > 90° (r = 0.493, P < .001).

CONCLUSION

After RTSA, patients significantly increase the frequency of arm elevation to higher angles. However, we found no differences in the amount of time spent at different elevation angles. Overall, after RTSA, >95% of the day was spent at elevation angles < 60° and <1% of the day was spent at >90° of elevation.

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.

Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method

BACKGROUND

Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

OBJECTIVE

Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.

METHODS

Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.

RESULTS

The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth.

CONCLUSIONS

In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

mHealth for pediatric chronic pain: state of the art and future directions

INTRODUCTION

Chronic pain conditions are common among children and engender cascading effects across social, emotional, and behavioral domains for the child and family. Mobile health (mHealth) describes the practice of delivering healthcare via mobile devices and may be an ideal solution to increase access and reach of evidence-based behavioral health interventions.

AREAS COVERED

The aim of this narrative review is to present a state-of-the-art overview of evidence-based mHealth efforts within the field of pediatric chronic pain and consider new and promising directions for study. Given the nascent nature of the field, published mHealth interventions in all stages of development are discussed. Literature was identified through a non-systematic search in PubMed and Google Scholar, and a review of reference lists of papers that were identified as particularly relevant or foundational (within and outside of the chronic pain literature).

EXPERT OPINION

mHealth is a promising interventional modality with early evidence suggesting it is primed to enhance behavioral health delivery and patient outcomes. There are many exciting future directions to be explored including drawing inspiration from digital health technology to generate new ways of thinking about the optimal treatment of pediatric chronic pain.

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Background

Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke.

Methods

This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods.

Results

Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score.

Conclusions

Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

Implementation of Wearable Sensing Technology for Movement: Pushing Forward into the Routine Physical Rehabilitation Care Field

While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples' homes and communities.

Adaptive gait segmentation algorithm for walking bout detection using tri-axial accelerometers

Gait analysis has many potential applications in understanding the activity profiles of individuals in their daily lives, particularly when studying the progression of recovery following injury, or motor deterioration in pathological conditions. One of the many challenges of conducting such analyses in the home environment is the correct and automatic identification of bouts of gait activity. To address this, a novel method for determining bouts of gait from accelerometer data recorded from the shank is presented. This method is fully automated and includes an adaptive thresholding approach which avoids the necessity for identifying subject-specific thresholds. The algorithm was tested on data recorded from 15 healthy subjects during self-selected slow, normal and fast walking speeds ranging from 0.48 ± 0.19 to 1.38 ± 0.33m/s and a single subject with PD walking at their normal walking speed (1.41 ± 0.08m/s) using accelerometers on the shanks. Intra-Class Correlation (ICC) confirmed high levels of agreement between bout onset/offset times and durations estimated using the algorithm, experimentally recorded stopwatch times and manual annotation for the healthy subjects (r=0.975, p <; 0.001; r=0.984, p<; 0.001) and moderate agreement for the PD subject (r=0.663, p<; 0.001). Mean absolute errors between accelerometer-derived and manually-annotated times were calculated, and ranged from 0.91 ± 0.05 s to 1.17 ± 2.26 s for bout onset detection, 0.80 ± 0.23 s to 2.41 ± 3.77 s for offset detection and 1.27 ± 0.13 s to 3.67 ± 4.59 s for bout durations.

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

Wearable Sensors Measure Ankle Joint Changes of Patients with Parkinson's Disease before and after Acute Levodopa Challenge

Background

Previous studies found levodopa could improve the activity of the ankle joints of patients with Parkinson's disease (PD). But ankle joint movement is composed of four motion ranges. The specific changes of four motion ranges in PD remain unknown.

Objective

The purpose of this study was to decompose the complex ankle joint movement, measure ankle joint changes before and after the acute levodopa challenge test (ALCT), and investigate the effects of these parameters on gait performance.

Methods

29 PD patients and 30 healthy control subjects (HC) completed the Instrumented Stand and Walk (ISAW) test and gait parameters were collected by the JiBuEn gait analysis system. The percentage of improvement of gait data and the UPDRS III in the on-drug condition (ON) were determined with respect to the off-drug condition (OFF).

Results

We observed a reduction in the heel strike angle (HS), 3-plantarflexion (3-PF) angle, and 4-dorsiflexion (4-DF) angle of ankle joints. We did not find significant difference in the toe-off angle (TO), 1-plantarflexion (1-PF) angle, and 2-dorsiflexion (2-DF) angle among three groups. Stride length improvement rate was significantly correlated with HS (r _s = 0.616, P < 0.001) and 3-PF (r _s = 0.639, P < 0.001) improvement rates. The improvement in the sum of rigidity items (UPDRS motor subsection item 22) was also correlated with HS (r _s = 0.389, P=0.037) and 3-PF (r _s = 0.373, P=0.046) improvement rates.

Conclusions

Exogenous levodopa supplementation can significantly reduce the rigidity of patients with PD, improve their 3-PF and 4-DF of ankle joint kinematic parameters, and ultimately enhance their gait.

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.

Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review

BACKGROUND

With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Upper limb (UL) impairment affects up to 77% of stroke survivors impacting on their ability to carry out everyday activities. However, despite an increase in research exploring these devices for UL rehabilitation, little is known of their effectiveness.

OBJECTIVE

This review aimed to assess the effectiveness of UL wearable technology for improving activity and participation in adult stroke survivors.

METHODS

Randomized controlled trials (RCTs) and randomized comparable trials of UL wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability, and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs and the Downs and Black Instrument for the quality of non RCTs.

RESULTS

In the review, we included 11 studies with collectively 354 participants at baseline and 323 participants at final follow-up including control groups and participants poststroke. Participants' stroke type and severity varied. Only 1 study found significant between-group differences for systems functioning and activity (P≤.02). The 11 included studies in this review had small sample sizes ranging from 5 to 99 participants at an average (mean) age of 57 years.

CONCLUSIONS

This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes and the appropriateness of the methodology for complex interventions. However, technology has the potential to measure outcomes, provide feedback, and engage users outside of clinical sessions. This could provide a platform for motivating stroke survivors to carry out more rehabilitation in the absence of a therapist, which could maximize recovery.

TRIAL REGISTRATION

PROSPERO CRD42017057715; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=57715.

Predictors of Clinically Important Changes in Actual and Perceived Functional Arm Use of the Affected Upper Limb After Rehabilitative Therapy in Chronic Stroke

OBJECTIVE

To identify the predictors of minimal clinically important changes in actual and perceived functional arm use of the affected upper limb after rehabilitative therapy.

DESIGN

Retrospective, observational cohort study.

SETTING

Outpatient rehabilitation settings.

PARTICIPANTS

A cohort of 94 patients with chronic stroke.

INTERVENTIONS

Patients received robot-assisted therapy, mirror therapy, or combined therapy for 4 weeks.

MAIN OUTCOME MEASURES

The primary outcome measures, assessed pre- and post intervention, included actual functional arm use measured by an accelerometer and perceived functional arm use measured by the Motor Activity Log (MAL). Candidate predictors included age, sex, time after stroke, side of stroke, and scores on the Fugl-Meyer Assessment, Modified Ashworth Scale, Medical Research Council scale, Wolf Motor Function Test, MAL (quality of movement), and Nottingham Extended Activities of Daily Living.

RESULTS

Being male (odds ratio [OR], 3.17; 95% CI, 1.13-8.87) and having a higher than median Medical Research Council score (OR, 2.68; 95% CI, 1.12-6.41) significantly predicted minimal clinically important changes assessed by an accelerometer. Fugl-Meyer Assessment scores (odds ratio, 1.06; 95% CI, 1.02-1.11) were a significant predictor of achieving clinically important changes in MAL amount of use. Wolf Motor Function Test (quality) scores (OR, 3.05; 95% CI, 1.38-6.77) could predict clinically important improvements in MAL quality of movement.

CONCLUSIONS

Predictors of clinically important changes in the use of the affected upper limb after robot-assisted therapy, mirror therapy, or combined therapy in patients with chronic stroke for 4 weeks differ for actual vs perceived use. Further studies are recommended to validate these findings in a larger sample.

Near-Field Communication Sensors

Near-field communication is a new kind of low-cost wireless communication technology developed in recent years, which brings great convenience to daily life activities such as medical care, food quality detection, and commerce. The integration of near-field communication devices and sensors exhibits great potential for these real-world applications by endowing sensors with new features of powerless and wireless signal transferring and conferring near field communication device with sensing function. In this review, we summarize recent progress in near field communication sensors, including the development of materials and device design and their applications in wearable personal healthcare devices. The opportunities and challenges in near-field communication sensors are discussed in the end.